What are the common techniques used for data cleaning?

The common techniques used for data cleaning include:

1. Handling missing values: This involves identifying and dealing with missing data points, which can be done by either removing the rows or columns with missing values, or by imputing the missing values using techniques like mean, median, or regression imputation.

2. Removing duplicates: This involves identifying and removing duplicate records from the dataset, ensuring that each observation is unique.

3. Handling outliers: Outliers are extreme values that deviate significantly from the rest of the data. They can be handled by either removing them if they are due to data entry errors, or by transforming them using techniques like winsorization or logarithmic transformation.

4. Standardizing and normalizing data: Standardization involves transforming the data to have zero mean and unit variance, while normalization involves scaling the data to a specific range (e.g., 0 to 1). These techniques help in comparing and analyzing variables on a similar scale.

5. Encoding categorical variables: Categorical variables need to be converted into numerical form for analysis. This can be done through techniques like one-hot encoding, label encoding, or ordinal encoding.

6. Handling inconsistent data: Inconsistent data refers to data that does not conform to predefined rules or constraints. This can be resolved by identifying and correcting inconsistencies, such as typos or formatting errors.

7. Feature selection: Feature selection involves identifying and selecting the most relevant features or variables for analysis, based on their importance or correlation with the target variable. This helps in reducing dimensionality and improving model performance.

8. Data integration: Data integration involves combining data from multiple sources or databases into a single dataset, ensuring consistency and eliminating redundancy.

These techniques help in preparing the data for analysis and modeling, ensuring that the data is accurate, complete, and in a suitable format for further processing.